Overview

Dataset statistics

Number of variables21
Number of observations2630
Missing cells962
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory431.6 KiB
Average record size in memory168.0 B

Variable types

Numeric10
Categorical11

Alerts

Age is highly correlated with WorkExperienceHigh correlation
WorkExperience is highly correlated with Age and 2 other fieldsHigh correlation
LastPromotion is highly correlated with CurrentProfileHigh correlation
CurrentProfile is highly correlated with WorkExperience and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with WorkExperienceHigh correlation
Age is highly correlated with WorkExperienceHigh correlation
WorkExperience is highly correlated with Age and 1 other fieldsHigh correlation
LastPromotion is highly correlated with CurrentProfileHigh correlation
CurrentProfile is highly correlated with LastPromotionHigh correlation
MonthlyIncome is highly correlated with WorkExperienceHigh correlation
Age is highly correlated with WorkExperienceHigh correlation
WorkExperience is highly correlated with Age and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with WorkExperienceHigh correlation
Department is highly correlated with EducationFieldHigh correlation
EducationField is highly correlated with DepartmentHigh correlation
Age is highly correlated with Designation and 2 other fieldsHigh correlation
Department is highly correlated with EducationFieldHigh correlation
EducationField is highly correlated with DepartmentHigh correlation
Designation is highly correlated with Age and 3 other fieldsHigh correlation
WorkExperience is highly correlated with Age and 4 other fieldsHigh correlation
LastPromotion is highly correlated with WorkExperience and 1 other fieldsHigh correlation
CurrentProfile is highly correlated with Designation and 2 other fieldsHigh correlation
MonthlyIncome is highly correlated with Age and 2 other fieldsHigh correlation
Age has 142 (5.4%) missing values Missing
Department has 58 (2.2%) missing values Missing
HomeToWork has 126 (4.8%) missing values Missing
Gender has 30 (1.1%) missing values Missing
HourlnWeek has 136 (5.2%) missing values Missing
Designation has 30 (1.1%) missing values Missing
SalaryHikelastYear has 94 (3.6%) missing values Missing
WorkExperience has 122 (4.6%) missing values Missing
LastPromotion has 57 (2.2%) missing values Missing
CurrentProfile has 134 (5.1%) missing values Missing
MonthlyIncome has 33 (1.3%) missing values Missing
EmployeeID is uniformly distributed Uniform
EmployeeID has unique values Unique
NumCompaniesWorked has 191 (7.3%) zeros Zeros
LastPromotion has 575 (21.9%) zeros Zeros
CurrentProfile has 264 (10.0%) zeros Zeros

Reproduction

Analysis started2022-06-17 19:22:49.332366
Analysis finished2022-06-17 19:23:14.970035
Duration25.64 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

EmployeeID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct2630
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6111315.5
Minimum6110001
Maximum6112630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:15.132628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6110001
5-th percentile6110132.45
Q16110658.25
median6111315.5
Q36111972.75
95-th percentile6112498.55
Maximum6112630
Range2629
Interquartile range (IQR)1314.5

Descriptive statistics

Standard deviation759.3599278
Coefficient of variation (CV)0.0001242547415
Kurtosis-1.2
Mean6111315.5
Median Absolute Deviation (MAD)657.5
Skewness0
Sum1.607275976 × 1010
Variance576627.5
MonotonicityStrictly increasing
2022-06-18T00:53:15.424450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61112321
 
< 0.1%
61100241
 
< 0.1%
61100261
 
< 0.1%
61120751
 
< 0.1%
61100281
 
< 0.1%
61120771
 
< 0.1%
61100301
 
< 0.1%
61120791
 
< 0.1%
61100321
 
< 0.1%
61120811
 
< 0.1%
Other values (2620)2620
99.6%
ValueCountFrequency (%)
61100011
< 0.1%
61100021
< 0.1%
61100031
< 0.1%
61100041
< 0.1%
61100051
< 0.1%
61100061
< 0.1%
61100071
< 0.1%
61100081
< 0.1%
61100091
< 0.1%
61100101
< 0.1%
ValueCountFrequency (%)
61126301
< 0.1%
61126291
< 0.1%
61126281
< 0.1%
61126271
< 0.1%
61126261
< 0.1%
61126251
< 0.1%
61126241
< 0.1%
61126231
< 0.1%
61126221
< 0.1%
61126211
< 0.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)1.8%
Missing142
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean37.42564309
Minimum18
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:15.681023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q131
median36
Q344
95-th percentile55
Maximum61
Range43
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.357662099
Coefficient of variation (CV)0.250033435
Kurtosis-0.491859837
Mean37.42564309
Median Absolute Deviation (MAD)6
Skewness0.3618880296
Sum93115
Variance87.56583996
MonotonicityNot monotonic
2022-06-18T00:53:15.921062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
34124
 
4.7%
35123
 
4.7%
36119
 
4.5%
31114
 
4.3%
30112
 
4.3%
32110
 
4.2%
3398
 
3.7%
2994
 
3.6%
3788
 
3.3%
4185
 
3.2%
Other values (34)1421
54.0%
(Missing)142
 
5.4%
ValueCountFrequency (%)
1810
 
0.4%
1925
 
1.0%
2016
 
0.6%
2121
 
0.8%
2227
 
1.0%
2324
 
0.9%
2440
1.5%
2543
1.6%
2657
2.2%
2771
2.7%
ValueCountFrequency (%)
613
 
0.1%
608
 
0.3%
5922
0.8%
5823
0.9%
5713
 
0.5%
5633
1.3%
5532
1.2%
5436
1.4%
5337
1.4%
5231
1.2%

TravelProfile
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
Rarely
1852 
Yes
529 
No
249 

Length

Max length6
Median length6
Mean length5.017870722
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowRarely
3rd rowRarely
4th rowRarely
5th rowRarely

Common Values

ValueCountFrequency (%)
Rarely1852
70.4%
Yes529
 
20.1%
No249
 
9.5%

Length

2022-06-18T00:53:16.177028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:16.330062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
rarely1852
70.4%
yes529
 
20.1%
no249
 
9.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Department
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing58
Missing (%)2.2%
Memory size20.7 KiB
Analytics
1675 
Sales
792 
Marketing
 
105

Length

Max length9
Median length9
Mean length7.768273717
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnalytics
2nd rowAnalytics
3rd rowSales
4th rowAnalytics
5th rowAnalytics

Common Values

ValueCountFrequency (%)
Analytics1675
63.7%
Sales792
30.1%
Marketing105
 
4.0%
(Missing)58
 
2.2%

Length

2022-06-18T00:53:16.519248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:16.683243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
analytics1675
65.1%
sales792
30.8%
marketing105
 
4.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HomeToWork
Real number (ℝ≥0)

MISSING

Distinct35
Distinct (%)1.4%
Missing126
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean11.42811502
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:16.849822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q317
95-th percentile28
Maximum123
Range122
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.84832599
Coefficient of variation (CV)0.7742594451
Kurtosis9.330962804
Mean11.42811502
Median Absolute Deviation (MAD)5
Skewness1.539657841
Sum28616
Variance78.29287283
MonotonicityNot monotonic
2022-06-18T00:53:17.072562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3315
 
12.0%
5217
 
8.3%
10165
 
6.3%
1152
 
5.8%
11137
 
5.2%
6125
 
4.8%
8123
 
4.7%
4116
 
4.4%
9113
 
4.3%
7103
 
3.9%
Other values (25)938
35.7%
(Missing)126
 
4.8%
ValueCountFrequency (%)
1152
5.8%
228
 
1.1%
3315
12.0%
4116
 
4.4%
5217
8.3%
6125
 
4.8%
7103
 
3.9%
8123
 
4.7%
9113
 
4.3%
10165
6.3%
ValueCountFrequency (%)
1231
 
< 0.1%
361
 
< 0.1%
342
 
0.1%
3224
 
0.9%
3118
 
0.7%
3042
1.6%
2936
1.4%
2831
1.2%
2752
2.0%
2664
2.4%

EducationField
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
Statistics
1040 
CA
857 
Marketing Diploma
291 
Engineer
263 
Other
145 

Length

Max length17
Median length10
Mean length7.601520913
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowStatistics
3rd rowCA
4th rowStatistics
5th rowCA

Common Values

ValueCountFrequency (%)
Statistics1040
39.5%
CA857
32.6%
Marketing Diploma291
 
11.1%
Engineer263
 
10.0%
Other145
 
5.5%
MBA34
 
1.3%

Length

2022-06-18T00:53:17.305602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:17.441561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
statistics1040
35.6%
ca857
29.3%
marketing291
 
10.0%
diploma291
 
10.0%
engineer263
 
9.0%
other145
 
5.0%
mba34
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Gender
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing30
Missing (%)1.1%
Memory size20.7 KiB
Male
1574 
Female
682 
F
344 

Length

Max length6
Median length4
Mean length4.127692308
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male1574
59.8%
Female682
25.9%
F344
 
13.1%
(Missing)30
 
1.1%

Length

2022-06-18T00:53:17.765879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:17.910922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male1574
60.5%
female682
26.2%
f344
 
13.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HourlnWeek
Real number (ℝ≥0)

MISSING

Distinct61
Distinct (%)2.4%
Missing136
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean57.86327185
Minimum12
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:18.083878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile34
Q149
median59
Q367
95-th percentile79
Maximum110
Range98
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.23450703
Coefficient of variation (CV)0.2287203369
Kurtosis-0.4597890926
Mean57.86327185
Median Absolute Deviation (MAD)9
Skewness-0.1798546976
Sum144311
Variance175.1521764
MonotonicityNot monotonic
2022-06-18T00:53:18.310997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5486
 
3.3%
6683
 
3.2%
6282
 
3.1%
5682
 
3.1%
6882
 
3.1%
5980
 
3.0%
6480
 
3.0%
5771
 
2.7%
6171
 
2.7%
5366
 
2.5%
Other values (51)1711
65.1%
(Missing)136
 
5.2%
ValueCountFrequency (%)
121
 
< 0.1%
141
 
< 0.1%
231
 
< 0.1%
251
 
< 0.1%
3019
0.7%
3117
 
0.6%
3245
1.7%
3327
1.0%
3424
0.9%
3531
1.2%
ValueCountFrequency (%)
1101
 
< 0.1%
1012
 
0.1%
991
 
< 0.1%
871
 
< 0.1%
8234
1.3%
8119
 
0.7%
8057
2.2%
7943
1.6%
7826
1.0%
7732
1.2%

Involvement
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
3
1510 
4
698 
1
183 
5
179 
2
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row5
5th row4

Common Values

ValueCountFrequency (%)
31510
57.4%
4698
26.5%
1183
 
7.0%
5179
 
6.8%
260
 
2.3%

Length

2022-06-18T00:53:18.511999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:18.620001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
31510
57.4%
4698
26.5%
1183
 
7.0%
5179
 
6.8%
260
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WorkLifeBalance
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
4
575 
5
553 
3
504 
1
501 
2
497 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
4575
21.9%
5553
21.0%
3504
19.2%
1501
19.0%
2497
18.9%

Length

2022-06-18T00:53:18.806957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:18.943959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4575
21.9%
5553
21.0%
3504
19.2%
1501
19.0%
2497
18.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Designation
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.2%
Missing30
Missing (%)1.1%
Memory size20.7 KiB
Executive
993 
Manager
920 
Senior Manager
391 
AVP
179 
VP
117 

Length

Max length14
Median length9
Mean length8.316153846
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExecutive
2nd rowExecutive
3rd rowExecutive
4th rowManager
5th rowSenior Manager

Common Values

ValueCountFrequency (%)
Executive993
37.8%
Manager920
35.0%
Senior Manager391
 
14.9%
AVP179
 
6.8%
VP117
 
4.4%
(Missing)30
 
1.1%

Length

2022-06-18T00:53:19.143997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:19.282998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
manager1311
43.8%
executive993
33.2%
senior391
 
13.1%
avp179
 
6.0%
vp117
 
3.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobSatisfaction
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
3
803 
5
520 
4
511 
1
451 
2
345 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row5
4th row3
5th row5

Common Values

ValueCountFrequency (%)
3803
30.5%
5520
19.8%
4511
19.4%
1451
17.1%
2345
13.1%

Length

2022-06-18T00:53:19.474000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:19.596964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3803
30.5%
5520
19.8%
4511
19.4%
1451
17.1%
2345
13.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ESOPs
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1
1328 
0
1302 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11328
50.5%
01302
49.5%

Length

2022-06-18T00:53:19.772179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:19.889299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
11328
50.5%
01302
49.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.222813688
Minimum0
Maximum20
Zeros191
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:20.008300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6636924
Coefficient of variation (CV)0.8265114454
Kurtosis1.18376494
Mean3.222813688
Median Absolute Deviation (MAD)1
Skewness1.119860297
Sum8476
Variance7.0952572
MonotonicityNot monotonic
2022-06-18T00:53:20.166297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1670
25.5%
2557
21.2%
3273
10.4%
4245
 
9.3%
0191
 
7.3%
5170
 
6.5%
7131
 
5.0%
8129
 
4.9%
6121
 
4.6%
990
 
3.4%
Other values (3)53
 
2.0%
ValueCountFrequency (%)
0191
 
7.3%
1670
25.5%
2557
21.2%
3273
10.4%
4245
 
9.3%
5170
 
6.5%
6121
 
4.6%
7131
 
5.0%
8129
 
4.9%
990
 
3.4%
ValueCountFrequency (%)
202
 
0.1%
181
 
< 0.1%
1050
 
1.9%
990
 
3.4%
8129
4.9%
7131
5.0%
6121
4.6%
5170
6.5%
4245
9.3%
3273
10.4%

OverTime
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
0
1762 
1
868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01762
67.0%
1868
33.0%

Length

2022-06-18T00:53:20.445923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:20.593673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01762
67.0%
1868
33.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SalaryHikelastYear
Real number (ℝ≥0)

MISSING

Distinct16
Distinct (%)0.6%
Missing94
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean20.63682965
Minimum16
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:20.716677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile16
Q118
median19
Q323
95-th percentile28
Maximum31
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.725519442
Coefficient of variation (CV)0.1805277024
Kurtosis-0.2349315835
Mean20.63682965
Median Absolute Deviation (MAD)2
Skewness0.8306511734
Sum52335
Variance13.87949511
MonotonicityNot monotonic
2022-06-18T00:53:20.893671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
18368
14.0%
17349
13.3%
19348
13.2%
20235
8.9%
16207
7.9%
21166
6.3%
23150
5.7%
22139
 
5.3%
24124
 
4.7%
25109
 
4.1%
Other values (6)341
13.0%
(Missing)94
 
3.6%
ValueCountFrequency (%)
16207
7.9%
17349
13.3%
18368
14.0%
19348
13.2%
20235
8.9%
21166
6.3%
22139
 
5.3%
23150
5.7%
24124
 
4.7%
25109
 
4.1%
ValueCountFrequency (%)
3116
 
0.6%
3039
 
1.5%
2938
 
1.4%
2879
3.0%
2785
3.2%
2684
3.2%
25109
4.1%
24124
4.7%
23150
5.7%
22139
5.3%

WorkExperience
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct41
Distinct (%)1.6%
Missing122
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean11.52551834
Minimum0
Maximum41
Zeros15
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:21.108671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q316
95-th percentile29
Maximum41
Range41
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.125348431
Coefficient of variation (CV)0.7049876796
Kurtosis0.9534174293
Mean11.52551834
Median Absolute Deviation (MAD)4
Skewness1.137623678
Sum28906
Variance66.02128713
MonotonicityNot monotonic
2022-06-18T00:53:21.293636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10253
 
9.6%
11185
 
7.0%
9183
 
7.0%
7175
 
6.7%
6169
 
6.4%
5137
 
5.2%
8132
 
5.0%
2126
 
4.8%
1112
 
4.3%
4101
 
3.8%
Other values (31)935
35.6%
(Missing)122
 
4.6%
ValueCountFrequency (%)
015
 
0.6%
1112
4.3%
2126
4.8%
360
 
2.3%
4101
3.8%
5137
5.2%
6169
6.4%
7175
6.7%
8132
5.0%
9183
7.0%
ValueCountFrequency (%)
414
 
0.2%
405
 
0.2%
383
 
0.1%
378
 
0.3%
3610
0.4%
359
0.3%
3414
0.5%
3313
0.5%
3220
0.8%
3111
0.4%

LastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct17
Distinct (%)0.7%
Missing57
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.661095997
Minimum0
Maximum16
Zeros575
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:21.464667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile10
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.303497117
Coefficient of variation (CV)1.241404715
Kurtosis3.260244084
Mean2.661095997
Median Absolute Deviation (MAD)1
Skewness1.88606408
Sum6847
Variance10.9130932
MonotonicityNot monotonic
2022-06-18T00:53:21.621672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1783
29.8%
0575
21.9%
2434
16.5%
3167
 
6.3%
4111
 
4.2%
794
 
3.6%
889
 
3.4%
585
 
3.2%
658
 
2.2%
936
 
1.4%
Other values (7)141
 
5.4%
(Missing)57
 
2.2%
ValueCountFrequency (%)
0575
21.9%
1783
29.8%
2434
16.5%
3167
 
6.3%
4111
 
4.2%
585
 
3.2%
658
 
2.2%
794
 
3.6%
889
 
3.4%
936
 
1.4%
ValueCountFrequency (%)
168
 
0.3%
1524
 
0.9%
1418
 
0.7%
1319
 
0.7%
1228
 
1.1%
1121
 
0.8%
1023
 
0.9%
936
 
1.4%
889
3.4%
794
3.6%

CurrentProfile
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct19
Distinct (%)0.8%
Missing134
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean4.506810897
Minimum0
Maximum18
Zeros264
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:21.795673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q38
95-th percentile10.25
Maximum18
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.585423854
Coefficient of variation (CV)0.7955567553
Kurtosis0.2019970143
Mean4.506810897
Median Absolute Deviation (MAD)2
Skewness0.7999550479
Sum11249
Variance12.85526421
MonotonicityNot monotonic
2022-06-18T00:53:21.944669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3372
14.1%
2356
13.5%
1299
11.4%
8283
10.8%
0264
10.0%
7217
8.3%
4204
7.8%
9154
5.9%
599
 
3.8%
1065
 
2.5%
Other values (9)183
7.0%
(Missing)134
 
5.1%
ValueCountFrequency (%)
0264
10.0%
1299
11.4%
2356
13.5%
3372
14.1%
4204
7.8%
599
 
3.8%
658
 
2.2%
7217
8.3%
8283
10.8%
9154
5.9%
ValueCountFrequency (%)
185
 
0.2%
1710
 
0.4%
164
 
0.2%
1511
 
0.4%
1413
 
0.5%
1321
 
0.8%
1229
 
1.1%
1132
 
1.2%
1065
2.5%
9154
5.9%

MaritalStatus
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
Single
933 
Married
841 
Divorsed
477 
M
379 

Length

Max length8
Median length7
Mean length5.961977186
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowM
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Single933
35.5%
Married841
32.0%
Divorsed477
18.1%
M379
14.4%

Length

2022-06-18T00:53:22.138483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-18T00:53:22.257481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
single933
35.5%
married841
32.0%
divorsed477
18.1%
m379
14.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1707
Distinct (%)65.7%
Missing33
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean21824.03889
Minimum1000
Maximum96000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-06-18T00:53:22.418480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile17258
Q118407
median20332
Q323587
95-th percentile33062.4
Maximum96000
Range95000
Interquartile range (IQR)5180

Descriptive statistics

Standard deviation5092.623158
Coefficient of variation (CV)0.2333492523
Kurtosis33.87942272
Mean21824.03889
Median Absolute Deviation (MAD)2131
Skewness3.317546033
Sum56677029
Variance25934810.63
MonotonicityNot monotonic
2022-06-18T00:53:22.619481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175647
 
0.3%
213467
 
0.3%
254006
 
0.2%
172936
 
0.2%
184046
 
0.2%
183426
 
0.2%
183195
 
0.2%
184085
 
0.2%
186915
 
0.2%
208285
 
0.2%
Other values (1697)2539
96.5%
(Missing)33
 
1.3%
ValueCountFrequency (%)
10001
< 0.1%
20001
< 0.1%
160521
< 0.1%
160811
< 0.1%
160911
< 0.1%
161181
< 0.1%
162001
< 0.1%
162321
< 0.1%
162741
< 0.1%
162812
0.1%
ValueCountFrequency (%)
960001
< 0.1%
950001
< 0.1%
359431
< 0.1%
359261
< 0.1%
358471
< 0.1%
358452
0.1%
358332
0.1%
357401
< 0.1%
357171
< 0.1%
356582
0.1%

Interactions

2022-06-18T00:53:10.418898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:51.946950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:54.160287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:55.960092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:57.849588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:59.679925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:01.740839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:04.221536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:06.331522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:08.393058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:10.608856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:52.170989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:54.346319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:56.152088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:58.028588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:59.896179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:01.945856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:04.443496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:06.548526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:08.594024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:10.786853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:52.395559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:54.508281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:56.323662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:58.207785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:00.103180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:02.140891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:04.678178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:06.737020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:08.789016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:10.992862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:52.648583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:54.683884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:56.508648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:58.408115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:00.304183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:02.336859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:04.924173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:06.945071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:09.014026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:11.172892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:52.888699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:54.849620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:56.690640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:58.576150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:00.500976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:02.891899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:05.134316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:07.145059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:09.193859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:11.399042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:53.134319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:55.046575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:56.904228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:58.772112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:00.723976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:03.106367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:05.348327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:07.357059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:09.407894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:11.625642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:53.382321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:55.238579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:57.097186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:58.959110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-18T00:53:07.567024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:09.610864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:11.845647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:53.568320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:55.399616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:57.270193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:59.124923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:01.117972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:03.555494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:05.720291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-18T00:53:09.800857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-06-18T00:52:53.780283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:55.588579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:57.469208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:59.313924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:01.330961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:03.797501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:05.927510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:07.980017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:10.016857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:12.333702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:53.977317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:55.780705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:57.666556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:52:59.499925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:01.544979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:04.027535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:06.137491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:08.193023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-06-18T00:53:10.222892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-06-18T00:53:22.820056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-18T00:53:23.605920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-18T00:53:23.998888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-18T00:53:24.436882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-18T00:53:24.846880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-18T00:53:12.737739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-18T00:53:13.595408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-18T00:53:14.264374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-18T00:53:14.654375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

EmployeeIDAgeTravelProfileDepartmentHomeToWorkEducationFieldGenderHourlnWeekInvolvementWorkLifeBalanceDesignationJobSatisfactionESOPsNumCompaniesWorkedOverTimeSalaryHikelastYearWorkExperienceLastPromotionCurrentProfileMaritalStatusMonthlyIncome
0611000118.0NoNaN9.0CAMale80.032Executive311017.00.00.00.0Single16904.0
1611000220.0RarelyAnalytics28.0StatisticsFemale59.013Executive112118.02.01.0NaNSingle18994.0
2611000350.0RarelyAnalytics19.0CAFemale76.033Executive505122.018.03.03.0M18587.0
3611000432.0RarelySales23.0StatisticsFemale73.052Manager304117.05.03.03.0Married20559.0
4611000539.0RarelyAnalytics7.0CAMale42.041Senior Manager514020.09.01.07.0Married24991.0
5611000648.0RarelyAnalytics5.0StatisticsMale78.043Senior Manager307019.027.04.08.0Single25999.0
6611000735.0YesMarketing26.0MBAF45.031Senior Manager5010121.012.01.03.0Married25950.0
7611000833.0RarelySales11.0EngineerFemale67.041Manager402117.0NaN12.010.0Married21228.0
8611000954.0RarelyAnalytics10.0StatisticsFemale67.033Manager303016.09.02.03.0M17897.0
9611001045.0NoSales7.0CAF57.045Senior Manager106017.023.012.08.0Single23865.0

Last rows

EmployeeIDAgeTravelProfileDepartmentHomeToWorkEducationFieldGenderHourlnWeekInvolvementWorkLifeBalanceDesignationJobSatisfactionESOPsNumCompaniesWorkedOverTimeSalaryHikelastYearWorkExperienceLastPromotionCurrentProfileMaritalStatusMonthlyIncome
2620611262126.0RarelyAnalytics6.0CAMale63.035Executive516019.07.01.03.0Divorsed20031.0
2621611262240.0YesAnalytics7.0CAFemale53.032Manager311019.0NaN4.09.0Single21042.0
2622611262345.0RarelyAnalytics6.0StatisticsMale61.033Executive515018.011.02.03.0Divorsed18362.0
2623611262433.0RarelySales7.0CAMale34.054Senior Manager202017.015.010.09.0Divorsed26400.0
2624611262533.0RarelyAnalytics1.0CAF65.014AVP304024.010.00.05.0Married31184.0
2625611262646.0RarelySales12.0Marketing DiplomaMale76.035Senior Manager515118.010.01.03.0Married26761.0
2626611262729.0RarelyAnalytics22.0CAMale80.044Executive502018.07.04.04.0Divorsed19196.0
2627611262844.0RarelyAnalytics8.0CAF42.041Senior Manager113019.024.05.017.0Married25248.0
26286112629NaNRarelyAnalytics11.0StatisticsFemaleNaN43Executive402018.02.01.01.0Single17261.0
2629611263050.0YesAnalytics1.0StatisticsF73.034VP413124.028.00.07.0Married33172.0